Event data means information that can be related to particular intervals of time. The time intervals may be "elapsed time," i.e., time related to a reference event, such as power-up of the data collecting device or start of the data collection process. The time intervals may instead be synchronized with a master standard, such as Greenwich mean time or an arbitrarily selected timekeeper, in which case the time is known as "synchronized time." Accepted time units (seconds, minutes, etc.) are used to measure both elapsed time and synchronized time.
One example of event data is vehicular traffic flow information sampled on a given street, which may be plotted with respect to synchronized time (e.g., time of day in hours and minutes) over the course of a day or week. Another example of event data is an electrocardiogram showing a patient's heart electrical activity, which may be plotted versus elapsed time from a starting or triggering event. Event data also includes the raw data upon which the plots or graphs are based, whether in digital, analog or any other form. The event data may be a continuous data stream, a discontinuous series of events, or a combination of continuous data and discrete events.
Data logging is one way of gathering event data. In data logging, a data gathering instrument monitors a process or situation and gathers and stores information about the process or situation for later analysis or archiving. During subsequent analysis, it may be important to determine the relationship of the recorded events and the time of the events' occurrence. For this reason, data loggers usually have a way of annotating the collected data with the time of collection.
For example, in an industrial chemical process, reaction temperatures may be recorded by a data logger continuously for quality assurance purposes to determine whether the temperatures stayed within predetermined bounds during the reaction. If the chemical reaction temperature did exceed expected limits, a time reference permits later analysis to determine when it did so, and for how long, to help identify the necessary remedial action. To illustrate: The data logger could note either (1) that the chemical process exceeded its temperature parameters 17.3 minutes from time the reaction data logging began (i.e., the data logger is measuring elapsed time) or (2) that the reaction began at 4:30:00 PM and the excursion occurred at 4:47:20 PM (i.e., the data logger is measuring synchronized time). In both scenarios, the temperature excursion of the monitored chemical process can be related to other events going on in the chemical plant.
As another example, it may be desirable to record highway traffic for road utilization analysis. Unattended data recorders may be used to note the passage of vehicles as time series of events that can later be analyzed when the recording is recovered from the monitoring site. A time stamp for each event allows later reconstruction and analysis of traffic flow. Elapsed time data can be used to determine the frequency of vehicle traffic. Synchronized time data can be used to correlate the traffic with other events, such as shift changes at nearby businesses.
Data logging can also occur during medical treatment and procedures. For example, emergency medical technicians delivering emergency care may use defibrillators to deliver electrical shocks to a patient's heart. Event data regarding the patient's physiological condition may be logged to provide information to later caregivers about the patient and about the care the patient received, such as the time required for the emergency medical technicians to reach the patient and the patient's response to the treatment.
As discussed above, data collected from data loggers may be analyzed to extract useful time-based information. Part of the event data analysis often requires reference to a local clock by the data user to place the time stamped on the collected event in the context of the data user's time. For example, if a portion of the collected event indicates that the event occurred at 4:00 PM, the data user must assume that the data logger clock and the data user's local clock indicated "4:00 PM" at the same time. In other words, the data user must assume that the data logger clock and the data user's local clock are synchronized. In addition, the data user must assume that the data logger's measure of a second or a minute is the same as the data user's local measure of a second or a minute so that the recorded time (whether elapsed time or synchronized time) may be interpreted in a meaningful way.
The synchronized time indicated by a data logger's clock may drift from the synchronized time indicated by the master timekeeper because of environmental conditions, mechanical problems, or other reasons. Also, the act of setting the data logger clock could introduce discrepancies between the time indicated by the data logger clock and the time indicated by the data user's clock, especially if the data logger clock is set by hand, or if the data logger's clock was not initially synchronized to the data user's clock prior to event data collection. These problems are compounded if a single data user receives event data from multiple data loggers, since each data logger clock may have been affected in different ways by environmental conditions, errors in initial setting, and the like.
For any measuring instrument or device, such as clocks, voltmeters, current meters, power meters, etc., calibration is typically performed on a periodic schedule in order to maintain the accuracy of the instrument. Calibration is the process of determining the absolute values corresponding to the gradations on an arbitrary or inaccurate scale or instrument when compared to a reference standard. Deviations from the reference standard that exist may result in changes being made to the instrument to bring the gradations to within an acceptable margin of error, or within the specifications set forth by the manufacturer.
In practice, it is common to refer to these steps (i.e., calibration and correction) together as "calibration". For example, instruments may be returned to the manufacturer for "calibration" according to a schedule. When the instrument is returned from the manufacturer, it is assumed that any deviations of gradations that fell outside of accepted error tolerances were corrected prior to returning the instrument.
In one example of this process, a series of measurements is taken by an instrument of unknown calibration. The same measurements are taken by an instrument of known calibration, typically a highly accurate reference instrument. The measurements are then compared to determine the amount of difference between the two measurements. Once the difference between the measurements is known, adjustments may be made to the device of unknown calibration to correct the gradations to correspond to the gradations of the reference device. As a result, each instrument provides the same measurement under identical conditions within an acceptable margin of error. In time, however, the calibrated instrument may accumulate additional error at which point it would be appropriate to again calibrate the instrument and make any necessary corrections to the gradations.
The frequency with which calibration and correction is performed varies from instrument to instrument. As will be appreciated by those skilled in the art, the need for calibration and correction does not necessarily correlate with the frequency with which the instrument is used. For example, an infrequently used instrument may require daily or weekly calibration and correction because of the amount of error that naturally accumulates in the instrument over time.
What is needed is a method of correcting measurements taken by a measuring instrument without necessarily calibrating and correcting the instrument's measuring gradations prior to use, wherein there is a high degree of confidence in the accuracy of the correction.